Haiqu Unveils Rivet: A Game-Changing Open-Source Toolkit for Quantum Computing

Quantum computing startup Haiqu has released Rivet, an open-source toolkit designed to streamline quantum computing workflows. The toolkit significantly reduces transpilation bottlenecks, a time-consuming step in preparing algorithms for execution on quantum hardware. Rivet allows developers to manage their computational resources better and optimize their research and prototyping cycles. The toolkit supports Qiskit, BQSKit, and Pytket, and plans to expand its supported stacks. Richard Givhan, CEO and Founder of Haiqu, stated that Rivet was developed to make research more efficient by minimizing time and money spent on transpilation.

Introduction to Rivet: A Quantum Computing Toolkit

Haiqu, a startup specializing in quantum computing middleware, has recently announced the release of Rivet, an open-source toolkit designed to streamline quantum computing workflows. The toolkit is aimed at developers working on a variety of modern quantum workflows, including error mitigation and quantum machine learning.

Overcoming Transpilation Bottlenecks

One of the key challenges in quantum computing is the process of transpilation, a preparatory step before the execution of an algorithm on quantum hardware. This process often takes a significant portion of execution time, creating a bottleneck in the workflow. Rivet addresses this issue by significantly reducing transpilation time for typical applications, from several hours to just a few minutes.

Rivet enhances the modularity and flexibility of industry-standard transpilation software, allowing quantum developers to better manage their computational resources and optimize their research and prototyping cycles. The toolkit also includes tools for increased control and convenience, enabling users to minimize the qubits used in their execution and debug their code with ease.

Rivet’s Transpiling Stack Selection

Rivet allows users to select the transpiling stack of their choice, for their entire circuit or just a section. It currently supports Qiskit, BQSKit, and Pytket, and plans to expand its supported stacks. The toolkit also provides tools such as caching and re-use and detailed control over transpilation passes, making it user-friendly despite its advanced functionality.

Rivet Transpiler: Features and Functions

The Rivet Transpiler allows users to design and implement fast automated modular transpilation routines with the transpilation stack of their choice. It includes convenience features, such as performance tracking and debugging. Rivet also allows circuits to be subdivided, and the parts transpiled separately while maintaining the correct relation to the other subparts. This feature, known as Subcircuit Transpilation and Stitching, allows for drastic saving of computational resources.

Rivet also offers Flexible Stack Selection, where users can transpile their entire circuit, or parts of a circuit, via one or a combination of transpilation passes from different stacks of their preference. This allows one to choose the optimal transpiling strategy for the given use case and circuit architecture.

Installation and Documentation

Rivet can be installed via pip, and supports only the Qiskit transpilation stack in its base version. To install with all stacks, users can run a specific command. The toolkit also provides the option to install only BQSKit or only Pytket support.

For more details about the Rivet Transpiler, users can check the reference documentation. The documentation includes tutorials on transpilation, as well as other features Rivet offers like Hashing. Examples of complex processes that could benefit from Rivet’s Subcircuit Transpilation and Stitching, such as Shadow State Tomography and Fourier Adders, are also provided.